






import copy
import random

from transformers import CLIPTokenizer




class 							snake_case_       (    lowerCamelCase__      ):

						"""simple docstring"""



						def __init__(self:					str ,					*__UpperCAmelCase:					List[str] ,					**__UpperCAmelCase:					Tuple   )    ->     Union[str, Any]:




											'''simple docstring'''



											super().__init__(*__snake_case ,					**__snake_case   )
											__a					:						Optional[Any]      =					{}



						def        UpperCAmelCase__					(self:					int ,					__UpperCAmelCase:					str ,					*__UpperCAmelCase:					List[str] ,					**__UpperCAmelCase:					List[str]   )    ->     Dict:




											'''simple docstring'''



											__a					:						Optional[int]      =					super().add_tokens(__snake_case ,					*__snake_case ,					**__snake_case   )
											if num_added_tokens == 0:
																raise ValueError(
																    f'The tokenizer already contains the token {placeholder_token}. Please pass a different'
																    " `placeholder_token` that is not already in the tokenizer."   )



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					Tuple ,					*__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					List[Any]=1 ,					**__UpperCAmelCase:					Union[str, Any]   )    ->     Tuple:




											'''simple docstring'''



											__a					:						int      =					[]
											if num_vec_per_token == 1:
																self.try_adding_tokens(__snake_case ,					*__snake_case ,					**__snake_case   )
																output.append(__snake_case   )
											else:
																__a					:						Tuple      =					[]
																for i in range(__snake_case   ):
																					__a					:						Union[str, Any]      =					placeholder_token + f'_{i}'
																					self.try_adding_tokens(__snake_case ,					*__snake_case ,					**__snake_case   )
																					output.append(__snake_case   )
																# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
											for token in self.token_map:
																if token in placeholder_token:
																					raise ValueError(
																					    f'The tokenizer already has placeholder token {token} that can get confused with'
																					    f' {placeholder_token}keep placeholder tokens independent'   )
											__a					:						str      =					output



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					Optional[Any]=False ,					__UpperCAmelCase:					Any=1.0   )    ->     Any:




											'''simple docstring'''



											if isinstance(__snake_case ,					__snake_case   ):
																__a					:						List[str]      =					[]
																for i in range(len(__snake_case   )   ):
																					output.append(self.replace_placeholder_tokens_in_text(text[i] ,					vector_shuffle=__snake_case   )   )
																return output
											for placeholder_token in self.token_map:
																if placeholder_token in text:
																					__a					:						Tuple      =					self.token_map[placeholder_token]
																					__a					:						List[Any]      =					tokens[: 1 + int(len(__snake_case   ) * prop_tokens_to_load   )]
																					if vector_shuffle:
																										__a					:						Tuple      =					copy.copy(__snake_case   )
																										random.shuffle(__snake_case   )
																					__a					:						List[Any]      =					text.replace(__snake_case ,					" ".join(__snake_case   )   )
											return text



						def __call__(self:					List[str] ,					__UpperCAmelCase:					List[str] ,					*__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					str=False ,					__UpperCAmelCase:					Optional[Any]=1.0 ,					**__UpperCAmelCase:					Optional[Any]   )    ->     Dict:




											'''simple docstring'''



											return super().__call__(
											    self.replace_placeholder_tokens_in_text(
											        __snake_case ,					vector_shuffle=__snake_case ,					prop_tokens_to_load=__snake_case   ) ,					*__snake_case ,					**__snake_case ,					)



						def        UpperCAmelCase__					(self:					Union[str, Any] ,					__UpperCAmelCase:					str ,					*__UpperCAmelCase:					str ,					__UpperCAmelCase:					str=False ,					__UpperCAmelCase:					Optional[int]=1.0 ,					**__UpperCAmelCase:					Optional[Any]   )    ->     List[str]:




											'''simple docstring'''



											return super().encode(
											    self.replace_placeholder_tokens_in_text(
											        __snake_case ,					vector_shuffle=__snake_case ,					prop_tokens_to_load=__snake_case   ) ,					*__snake_case ,					**__snake_case ,					)










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   List[str]					=		logging.get_logger(__name__)

lowerCAmelCase:   Union[str, Any]					=		{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """lilt"""







    def __init__( self					:    str		,     __snake_case					:    Dict=3_05_22		,     __snake_case					:    int=7_68		,     __snake_case					:    Union[str, Any]=12		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Dict=30_72		,     __snake_case					:    Tuple="gelu"		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    List[str]=5_12		,     __snake_case					:    List[str]=2		,     __snake_case					:    str=0.02		,     __snake_case					:    Dict=1e-1_2		,     __snake_case					:    int=0		,     __snake_case					:    Any="absolute"		,     __snake_case					:    Tuple=None		,     __snake_case					:    Tuple=4		,     __snake_case					:    List[str]=10_24		,     **__snake_case					:    Union[str, Any]		,     ):
      super().__init__(pad_token_id=__snake_case		,     **__snake_case      )

      a		:   str	  =	vocab_size
      a		:   Optional[Any]	  =	hidden_size
      a		:   Union[str, Any]	  =	num_hidden_layers
      a		:   Tuple	  =	num_attention_heads
      a		:   Optional[Any]	  =	hidden_act
      a		:   Union[str, Any]	  =	intermediate_size
      a		:   Optional[Any]	  =	hidden_dropout_prob
      a		:   Tuple	  =	attention_probs_dropout_prob
      a		:   str	  =	max_position_embeddings
      a		:   Any	  =	type_vocab_size
      a		:   Union[str, Any]	  =	initializer_range
      a		:   int	  =	layer_norm_eps
      a		:   Any	  =	position_embedding_type
      a		:   Any	  =	classifier_dropout
      a		:   str	  =	channel_shrink_ratio
      a		:   Optional[Any]	  =	max_ad_position_embeddings